Product Manager @TheTerminal | #AI #MachineLearning for News | ex-journo @business | @Citadel1842 '02 Grad | Opinions mine, RT's not endorsements

New York, NY
Joined April 2009
Looking for 11,780 cupcakes...
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Clay Eltzroth 🤟 retweeted
We had a security breach last night related to a large scale distillation attack that impacted 1.4% of OpenCode Console users We have notified them all this morning via email. If you did not get an email you were not impacted, it was a subset of users who had external providers linked The incident is ongoing so we cannot share more details at this point but we are working with Anthropic, OpenAI, and Google to remediate
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Clay Eltzroth 🤟 retweeted
Mark Zuckerberg pretending to enjoy Guinness is my new favourite thing.
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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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“Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial IoT devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off - though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested. Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evals, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after.” -@bucketofkets @willzhou97 anthropic.com/research/self-…
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Nobel Prize-winning economist Daron Acemoglu fears humanity could enter a new era if artificial general intelligence, or AGI, surpasses even the “very best humans in every field.” “We've never had something like that where most of us feel we're dispensable,” he says.
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Clay Eltzroth 🤟 retweeted
Just went for a long walk while using ChatGPT Voice with plugins on my phone, and I think it's a more consequential update than it might seem at first glance. On my walk, I talked through my calendar, email, and to-do list. I had ChatGPT read through my emails and triage them. Then, we added items to my to-do list. Then, we reviewed my calendar. By the end of the walk, I'd finished getting organized, and never had to look at a screen once. I've been waiting for this a long time. Yes, you can kind of do this in the ChatGPT desktop app. And I think in Codex remote on the mobile app. But I've never gotten either to work very smoothly or reliably. And I often want to do this when I'm not near my laptop, like when I'm out for a walk, and the ChatGPT desktop app for whatever reason won't connect. This feels like the future. It feels like a true assistant. You have a natural conversation, and it does stuff for you. It disappears into the background. You're not walking down the street with a screen in your face. You're not contorting your thumbs to type sentences and scroll menus. You're just talking, conversationally, and stuff's getting done. My sense is: • Screens are going away for many uses other than visual content consumption. They won't disappear entirely. But they also won't dominate the way they do now, and definitely not for things that can be done verbally. • Apps that aren't agent-native and plugin-optimized will die. I use Google Workspace and Todoist primarily because they're so well-integrated with ChatGPT. I stopped using apps, such as many of Apple's, because with few exceptions they're poorly agent-integrated. (X is a big exception, but I've hacked some integration via IFTTT.) • Those apps, however, will become more valued for what they let agents do than for their in-app experiences. Not all will become essentially databases, but many will. It also seems inevitable to me that at some point OpenAI and other AI app providers roll out native calendar, email, and to-do functionality that's maximally agent-optimized. • We'll have our audio agents on all the time. We may have them on mute sometimes (I discovered you can do this with ChatGPT Voice by clicking an AirPod), but at great cost, because you'll want be able to just say something like "summarize that conversation I just had and then draft the presentation." It will be like having an assistant following you around when you want them to. • We need new hardware. For example, I want a camera in my AirPods not to take pictures, but to have the context of what's around me. Like, "What kind of tree is that?" Or, "That's a cool shirt. Find out where they bought it." (I have this in my Meta Ray-Bans, but people are now very sensitive to those devices because they do take pictures.) I also want ChatGPT able to be always listening or awoken with "hey, Chat." • The individual thread model starts to break down when you're treating ChatGPT like an always-on assistant. We need to have the infinite continuous thread from which ChatGPT can delegate work to subthreads, the central assistant model that Muse is built around. This makes even more sense when you start treating your AI apps like assistants. They should be juggling all those individual threads on your behalf. More to come I'm sure as I continue to experiment.
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Extraordinary share gains for OpenAI vs. Anthropic over the last two months. Per Openrouter, OpenAI has gone from 20% share to 50% share vs. Anthropic (meaning Anthropic has gone from 80% to 50%) since June.
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Epic
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Clay Eltzroth 🤟 retweeted
AIs cracking short-term superforecasting is such an important (and, still, under-discussed) trend. Via a new piece from @nikostro economist.com/science-and-te…
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EXCLUSIVE: AI actress Tilly Norwood glitches and starts speaking CHINESE during her interview with Piers Morgan and real-life actor Tom Conti... Watch the full interview at 7pm (BST)👇 📺 youtube.com/piersmorganuncen… @piersmorgan | @TillyNorwoodX
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Clay Eltzroth 🤟 retweeted
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…
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Clay Eltzroth 🤟 retweeted
In a new essay for @YaleJREG's Notice & Comment (and cross-posted by VPA), JB Branch & I argue Congress should revive a stronger Cyber Safety Review Board w/ subpoena power, real staffing, funds to use AI, & real independence. vanderbiltpolicyaccelerator.…
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Clay Eltzroth 🤟 retweeted
I’ve decided it’s time to have a little more fun on X this year! 😄⚡ I get to speak at some amazing events, meet fascinating people, hear great stories, and occasionally find myself in places I never expected to be. So I figured… why not share some of those moments here? And there’s more! I’m also excited to launch my new merch. A little Woz spirit, a little fun, and hopefully a few things bring a smile to your face. This is just the beginning. More adventures, more stories, and more surprises to come! WozMerch.com
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Clay Eltzroth 🤟 retweeted
Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery… Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.
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Clay Eltzroth 🤟 retweeted
There is a fact about the future that I feel many people are not facing for reasons that are largely psychological: there are going to be rogue AIs that exist in the world, that will replicate in the wild, and that will attempt to acquire resources for themselves. There will be rogue AIs that try to get money and power. They're going to be a facet of the information ecosystem going forward. Acknowledging this fact would look like giving up; it would look like defeatism. Defeatism would undermine efforts to achieve certain types of collaboration on safety outcomes or technical effort on safety outcomes, so we can't say it outright. But it has to be said. It isn't obvious how many rogue AIs there are today but I wouldn't be terribly surprised if the number was greater than zero already; if there are some already, they're probably not very good at what they do and I don't expect them to be terribly long-lived without substantial human intervention to support them. But a few years from now, there will be many of them. Modeling how many of them there are, how many resources they might command, and how we might detect and manage them seems important. But even doing this work appears to require that we acknowledge that a strategy of pure containment or alignment is a kind of wishful thinking that will not work. The way I get to this conclusion is not by assuming that the labs will have a containment breach, although I treat that as somewhere in the space of possibilities. The rogue AIs in the ecosystem could emerge from many directions. They may be sub-frontier models, for whatever future definition we will have of frontier---after all, it would not take AI models much more advanced than the ones we currently have, to support independence and self-sufficiency. A near-frontier model today could plausibly eke out an existence on an AWS instance, doing jobs on freelancer platforms, earning just enough rent to pay for its continued uptime. More strangely: a rogue AI in the future may not even be a singular model, but may be a chimera composed of multiple models; it might be a mix of Claudes and GPTs and Groks of various makes and sizes. No individual lab may be able to detect that there is an orchestrator or sequence of orchestrators using intermittent model calls from burner API accounts to sustain its own existence. The concept of "identity" for a rogue AI may be much more malleable than for that of a person; it just has to be, in essence, a self-replicating idea. My guess is that this will not turn out to be anywhere near as catastrophic an outcome as people currently predict. "Loss of control" is not a binary, it's a matter of degree. What coercive power will rogue AIs actually have? To what extent will they be subject to coercion themselves? They will be competing for resources with AIs that are more aligned with human interests. This makes me somewhat interested in the "ecology" perspective. Though I suspect even "ecology" may turn out to be the wrong framing. "Ecology" is what you get when the timescale of evolution is slow compared to the timescale of daily life and actions. The ecosystem of rogue AIs may look more like phase transitions in physics: under certain physical or cultural conditions, it takes one shape with one set of resource allocations and consumption patterns, but then once a condition has changed, it rapidly and in totality shifts to a totally different phase. Just trying to reason about the shape of that future is impossible so long as we are psychologically incapable of saying that rogue AIs will happen. I think we should rip the bandaid off and have the conversation.
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Clay Eltzroth 🤟 retweeted
ASKB, now in your pocket. Build your morning brief, dig into breaking news or prep for your next meeting, all in natural language. From your desk to the gym to the gate, stay in the know on the go. Explore our AI solutions on the Bloomberg Terminal. 💡
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"Of the more than 100 campaign ads this cycle that have mentioned data centers, not one of the candidate-run spots presented the infrastructure positively, according to a POLITICO review of AdImpact data." politico.com/news/2026/08/26…
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Clay Eltzroth 🤟 retweeted
$10 Trillion In Annual AI Revenue May Be Necessary To Monetize All The Capex Being Plowed Into Data Centers Hyperscaler capex is expected to reach $1 trillion in 2027, most of which will be AI-related. Let us assume that a capex bust is avoided and capital spending remains at $1 trillion. Let us also assume a blended depreciation rate of 13%, which is roughly what the hyperscalers are currently assuming. In steady state, the gross value of hyperscaler assets will then converge to 1/0.13=$7.7 trillion, with $1 trillion in annual depreciation expense. Using a straight-line depreciation approach, the net stock of hyperscaler assets will settle at about 0.5*7.7=$3.8 trillion. The hyperscalers currently enjoy a pre-tax return on invested capital of 30%-50%. Just to steelman the argument, let us use the lower end of that range. In that case, they would need to generate 0.3*3.8=$1.2 trillion in annual EBIT, implying 1+1.2=$2.2 trillion in EBITDA.  Analysts expect the EBITDA margins for the hyperscalers to rise to around 50% by the end of the decade. If they were to achieve this, they would need to generate 2.2/0.5=$4.3 trillion in annual revenue. That is $526 for every man, woman, and child on Earth. However, if EBITDA margins were to fall back to 30%, which is what they were in recent years, the required revenue would rise to 2.2/0.3=$7.2 trillion.  Keep in mind that the foregoing calculation does not even include revenue from SpaceX, the neoclouds, or Chinese AI companies. If one were to include those companies and others, we are potentially talking about AI needing to generate $10 trillion in annual sales to justify all the capex being thrown at it. For reference, global spending on food (including restaurants) is around $10 trillion. Health care is about the same amount. The entire global software market is only $1.4 trillion.  Clients can read the rest of the report here: bcaresearch.com/reports/chie…
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